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We investigate the three-dimensional compressible Euler-Maxwell system, a model for simulating the transport of electrons interacting with propagating electromagnetic waves in semiconductor devices. First, we show the global well-posedness…

Analysis of PDEs · Mathematics 2024-07-02 Timothée Crin-Barat , Yue-Jun Peng , Ling-Yun Shou , Jiang Xu

In this paper, we propose a linear and monolithic finite element method for the approximation of an incompressible viscous fluid interacting with an elastic and deforming plate. We use the arbitrary Lagrangian-Eulerian (ALE) approach that…

Numerical Analysis · Mathematics 2023-01-13 Sebastian Schwarzacher , Bangwei She , Karel Tuma

A new regularisation of the shallow water (and isentropic Euler) equations is proposed. The regularised equations are non-dissipative, non-dispersive and possess a variational structure. Thus, the mass, the momentum and the energy are…

Fluid Dynamics · Physics 2020-02-20 Didier Clamond , Denys Dutykh

We investigate a weighted Multilevel Richardson-Romberg extrapolation for the ergodic approximation of invariant distributions of diffusions adapted from the one introduced in~[Lemaire-Pag\`es, 2013] for regular Monte Carlo simulation. In a…

Probability · Mathematics 2016-07-05 Gilles Pagès , Fabien Panloup

In order to inherit numerically the ergodicity of the damped stochastic nonlinear Schr\"odinger equation with additive noise, we propose a fully discrete scheme, whose spatial direction is based on spectral Galerkin method and temporal…

Numerical Analysis · Mathematics 2016-06-07 Chuchu Chen , Jialin Hong , Xu Wang

This paper mainly investigates the strong convergence and stability of the truncated Euler-Maruyama (EM) method for stochastic differential delay equations with variable delay whose coefficients can be growing super-linearly. By…

Numerical Analysis · Mathematics 2021-08-10 Shounian Deng , Chen Fei , Weiyin Fei , Xuerong Mao

Fast Incremental Expectation Maximization (FIEM) is a version of the EM framework for large datasets. In this paper, we first recast FIEM and other incremental EM type algorithms in the {\em Stochastic Approximation within EM} framework.…

Machine Learning · Computer Science 2021-01-01 Gersende Fort , P. Gach , E. Moulines

We establish higher-order nonasymptotic expansions for a difference between probability distributions of sums of i.i.d. random vectors in a Euclidean space. The derived bounds are uniform over two classes of sets: the set of all Euclidean…

Statistics Theory · Mathematics 2022-11-30 Mayya Zhilova

We consider a nonlocal evolution equation representing the continuum limit of a large ensemble of interacting particles on graphs forced by noise. The two principle ingredients of the continuum model are a nonlocal term and Q-Wiener process…

Numerical Analysis · Mathematics 2022-04-05 Georgi Medvedev , Gideon Simpson

We propose a straightforward and effective method for discretizing multi-dimensional diffusion processes as an extension of Milstein scheme. The new scheme is explicitly given and can be simulated using Gaussian variates, requiring the same…

Numerical Analysis · Mathematics 2024-09-04 Yuga Iguchi , Toshihiro Yamada

An asymptotic preserving and energy stable scheme for the Euler-Poisson system under the quasineutral scaling is designed and analysed. Correction terms are introduced in the convective fluxes and the electrostatic potential, which lead to…

Numerical Analysis · Mathematics 2023-07-24 K. R. Arun , Rahuldev Ghorai , Mainak Kar

In this work, we focus on the development of high-order Implicit-Explicit (IMEX) finite volume numerical methods for plasmas in quasineutral regimes. At large temporal and spatial scales, plasmas tend to be quasineutral, meaning that the…

Numerical Analysis · Mathematics 2024-09-10 Nicolas Crouseilles , Giacomo Dimarco , Saurav Samantaray

This paper focuses on studying the convergence rate of the density function of the Euler--Maruyama (EM) method, when applied to the overdamped generalized Langevin equation with fractional noise which serves as an important model in many…

Numerical Analysis · Mathematics 2024-05-21 Xinjie Dai , Diancong Jin

Regularized system identification is the major advance in system identification in the last decade. Although many promising results have been achieved, it is far from complete and there are still many key problems to be solved. One of them…

Systems and Control · Electrical Eng. & Systems 2023-04-05 Yue Ju , Biqiang Mu , Lennart Ljung , Tianshi Chen

We establish some new non-asymptotical lower bounds for deviation of regular unbiased estimation of unknown parameter from its true value in different norms, alike the classical Rao-Kramer's inequality. We show that if the new norm is…

Statistics Theory · Mathematics 2014-07-17 E. Ostrovsky , L. Sirota

In this paper we investigate explicit numerical approximations for stochastic differential delay equations (SDDEs) under a local Lipschitz condition by employing the adaptive Euler-Maruyama (EM) method. Working in both finite and infinite…

Probability · Mathematics 2023-08-31 Ulises Botija-Munoz , Chenggui Yuan

An issue for molecular dynamics simulations is that events of interest often involve timescales that are much longer than the simulation time step, which is set by the fastest timescales of the model. Because of this timescale separation,…

Statistical Mechanics · Physics 2024-08-15 John Strahan , Chatipat Lorpaiboon , Jonathan Weare , Aaron R. Dinner

This paper deals with the backward Euler method applied to semilinear parabolic stochastic partial differential equations (SPDEs) driven by additive noise. The SPDE is discretized in space by the finite element method and in time by the…

Numerical Analysis · Mathematics 2020-01-01 Jean Daniel Mukam , Antoine Tambue

We consider the problem of sampling from a high-dimensional target distribution $\pi_\beta$ on $\mathbb{R}^d$ with density proportional to $\theta\mapsto e^{-\beta U(\theta)}$ using explicit numerical schemes based on discretising the…

Probability · Mathematics 2024-06-13 Ariel Neufeld , Matthew Ng Cheng En , Ying Zhang

We study Bayesian inverse problems with mixed noise, modeled as a combination of additive and multiplicative Gaussian components. While traditional inference methods often assume fixed or known noise characteristics, real-world…

Machine Learning · Computer Science 2025-10-17 Paul Hagemann , Robert Gruhlke , Bernhard Stankewitz , Claudia Schillings , Gabriele Steidl